• this file has been update 20220926 with the >=21 donors for DCM comparison.
#library(GEOquery)  ## go to https://github.com/seandavi/GEOquery for installation details
#library(R.utils)
library(reshape2)
library(ggplot2)
library(limma)
library(dplyr)
library(tidyverse)
#library(DMwR)
library(readxl)
library(MultiAssayExperiment)
library(S4Vectors)
library(SummarizedExperiment)
library(DT)
library(randomForest)
library(e1071)
library("bnstruct")
library(rminer)
library(ggpubr)
setwd("~/Dropbox (Sydney Uni)/Diabetic cardiomyopathy/YUNWEI USE THIS/clean_up_folder/analysis")
# install.packages("VANData_1.0.0.tgz", repos=NULL,type="source")
# install.packages("VAN_1.0.0.tgz", repos=NULL,type="source") 

library(R.utils)
library(reshape2)
library(ggplot2)
library(limma)
library(dplyr)
library(tidyverse)
library(DMwR)
library(readxl)
library(magrittr)
library(tidyverse)
library(gage)
library(knitr)
library(DT)
library(janitor)
library(KEGGREST)
library(MASS)
library("AnnotationDbi")
library("org.Hs.eg.db")
library(dplyr)
library(reshape)
library(ggplot2)
library(plyr)
library(gtable)
library(grid)
library(pheatmap)
library(reshape2)
library(plotly)
library(UpSetR)
library(MatchIt)
require(pathview)
library(STRINGdb)
## Kegg sets data
require(gage)
require(gageData)
# kegg = kegg.gsets(species = "hsa", id.type = "kegg")
# kegg.sets.hsa = kegg$kg.sets

# go.hs=go.gsets(species="human")
# go.bp=go.hs$go.sets[go.hs$go.subs$BP]
# go.mf=go.hs$go.sets[go.hs$go.subs$MF]
# go.bpmf=go.hs$go.sets[go.hs$go.subs$BP&go.hs$go.subs$MF] #not equal, wired
# go.bpmf2=append(go.bp,go.mf)
require(gage)
require(gageData)
library(clusterProfiler)
library(enrichplot)

1 DCM-DM VS Donor

1.1 DE analysis and standard pipeline

## [1] 155 107
## [1] 37 14
## [1] 29 14
##  [1] "31_LV_DCM_Yes_22_F"   "32_LV_DCM_Yes_31_F"   "33_LV_DCM_Yes_53_M"  
##  [4] "34_LV_DCM_Yes_56_M"   "35_LV_DCM_Yes_56_M"   "36_LV_DCM_Yes_58_M"  
##  [7] "37_LV_DCM_Yes_59_M"   "38_LV_DCM_Yes_61_F"   "39_LV_DCM_Yes_63_F"  
## [10] "90_LV_Donor_No_21_M"  "91_LV_Donor_No_23_F"  "92_LV_Donor_No_23_M" 
## [13] "93_LV_Donor_No_25_F"  "94_LV_Donor_No_47_M"  "95_LV_Donor_No_47_F" 
## [16] "96_LV_Donor_No_48_M"  "97_LV_Donor_No_49_F"  "98_LV_Donor_No_51_F" 
## [19] "99_LV_Donor_No_53_M"  "100_LV_Donor_No_53_F" "101_LV_Donor_No_54_M"
## [22] "102_LV_Donor_No_54_F" "104_LV_Donor_No_55_F" "105_LV_Donor_No_56_M"
## [25] "106_LV_Donor_No_60_M" "107_LV_Donor_No_62_M" "108_LV_Donor_No_62_F"
## [28] "109_LV_Donor_No_65_M" "110_LV_Donor_No_65_F"
## [1] 155  29
## [1] 155  29
## [1] 154  29
##  [1] "31_LV_DCM_Yes_22_F"   "32_LV_DCM_Yes_31_F"   "33_LV_DCM_Yes_53_M"  
##  [4] "34_LV_DCM_Yes_56_M"   "35_LV_DCM_Yes_56_M"   "36_LV_DCM_Yes_58_M"  
##  [7] "37_LV_DCM_Yes_59_M"   "38_LV_DCM_Yes_61_F"   "39_LV_DCM_Yes_63_F"  
## [10] "90_LV_Donor_No_21_M"  "91_LV_Donor_No_23_F"  "92_LV_Donor_No_23_M" 
## [13] "93_LV_Donor_No_25_F"  "94_LV_Donor_No_47_M"  "95_LV_Donor_No_47_F" 
## [16] "96_LV_Donor_No_48_M"  "97_LV_Donor_No_49_F"  "98_LV_Donor_No_51_F" 
## [19] "99_LV_Donor_No_53_M"  "100_LV_Donor_No_53_F" "101_LV_Donor_No_54_M"
## [22] "102_LV_Donor_No_54_F" "104_LV_Donor_No_55_F" "105_LV_Donor_No_56_M"
## [25] "106_LV_Donor_No_60_M" "107_LV_Donor_No_62_M" "108_LV_Donor_No_62_F"
## [28] "109_LV_Donor_No_65_M" "110_LV_Donor_No_65_F"
## [1] 2.792392
## [1] 154  10
## [1] 154  10
## [1] 154
## [1] 154   9
## [1] 154   9
## [1] 137
## [1] 143

2 DCM-NO DM VS Donor

2.1 DE analysis and standard pipeline

## [1] 155 107
## [1] 46 14
## [1] 38 14
##  [1] "40_LV_DCM_No_43_F"    "41_LV_DCM_No_43_M"    "42_LV_DCM_No_45_M"   
##  [4] "43_LV_DCM_No_49_M"    "44_LV_DCM_No_50_F"    "45_LV_DCM_No_51_M"   
##  [7] "46_LV_DCM_No_53_F"    "47_LV_DCM_No_53_M"    "48_LV_DCM_No_54_M"   
## [10] "49_LV_DCM_No_54_M"    "50_LV_DCM_No_55_M"    "51_LV_DCM_No_56_M"   
## [13] "52_LV_DCM_No_58_F"    "53_LV_DCM_No_58_M"    "54_LV_DCM_No_60_F"   
## [16] "55_LV_DCM_No_62_M"    "56_LV_DCM_No_52_M"    "90_LV_Donor_No_21_M" 
## [19] "91_LV_Donor_No_23_F"  "92_LV_Donor_No_23_M"  "93_LV_Donor_No_25_F" 
## [22] "94_LV_Donor_No_47_M"  "95_LV_Donor_No_47_F"  "96_LV_Donor_No_48_M" 
## [25] "97_LV_Donor_No_49_F"  "98_LV_Donor_No_51_F"  "99_LV_Donor_No_53_M" 
## [28] "100_LV_Donor_No_53_F" "101_LV_Donor_No_54_M" "102_LV_Donor_No_54_F"
## [31] "103_LV_DCM_No_44_M"   "104_LV_Donor_No_55_F" "105_LV_Donor_No_56_M"
## [34] "106_LV_Donor_No_60_M" "107_LV_Donor_No_62_M" "108_LV_Donor_No_62_F"
## [37] "109_LV_Donor_No_65_M" "110_LV_Donor_No_65_F"
## [1] 155  38
## [1] 155  38
## [1] 154  38
##  [1] "40_LV_DCM_No_43_F"    "41_LV_DCM_No_43_M"    "42_LV_DCM_No_45_M"   
##  [4] "43_LV_DCM_No_49_M"    "44_LV_DCM_No_50_F"    "45_LV_DCM_No_51_M"   
##  [7] "46_LV_DCM_No_53_F"    "47_LV_DCM_No_53_M"    "48_LV_DCM_No_54_M"   
## [10] "49_LV_DCM_No_54_M"    "50_LV_DCM_No_55_M"    "51_LV_DCM_No_56_M"   
## [13] "52_LV_DCM_No_58_F"    "53_LV_DCM_No_58_M"    "54_LV_DCM_No_60_F"   
## [16] "55_LV_DCM_No_62_M"    "56_LV_DCM_No_52_M"    "103_LV_DCM_No_44_M"  
## [19] "90_LV_Donor_No_21_M"  "91_LV_Donor_No_23_F"  "92_LV_Donor_No_23_M" 
## [22] "93_LV_Donor_No_25_F"  "94_LV_Donor_No_47_M"  "95_LV_Donor_No_47_F" 
## [25] "96_LV_Donor_No_48_M"  "97_LV_Donor_No_49_F"  "98_LV_Donor_No_51_F" 
## [28] "99_LV_Donor_No_53_M"  "100_LV_Donor_No_53_F" "101_LV_Donor_No_54_M"
## [31] "102_LV_Donor_No_54_F" "104_LV_Donor_No_55_F" "105_LV_Donor_No_56_M"
## [34] "106_LV_Donor_No_60_M" "107_LV_Donor_No_62_M" "108_LV_Donor_No_62_F"
## [37] "109_LV_Donor_No_65_M" "110_LV_Donor_No_65_F"
## [1] 2.41218
## [1] 154  10
## [1] 154  10
## [1] 154   9
## [1] 154   9
## [1] 137
## [1] 143

3 Figure3B

## [1] 71 11
## [1] 71 11
common_name=c(ihddm_dt2$Description,ihdnodm_dt2$Description)
plotmerge=merge(ihddm_dt2,ihdnodm_dt2,by="Description")
plotmerge$pvalue.x=-log10(as.numeric(plotmerge$pvalue.x))
plotmerge$pvalue.y=-log10(as.numeric(plotmerge$pvalue.y))
# annotation=read_excel("/Users/yzha0247/Dropbox (Sydney Uni)/Diabetic cardiomyopathy/YUNWEI USE THIS/Yunwei202110/IHD-DM paper figure 3a pathways to annotate.xlsx")
# plotmerge$mito=as.character(ifelse(plotmerge$Description%in% ben_mito$Description,1,0))
# plotmerge$annotation=as.character(ifelse(plotmerge$Description%in% annotation$`Amino acid metabolism`,1,0))
# ggplotly(ggplot(plotmerge,aes(pvalue.x,pvalue.y,label=Description,color=mito))+geom_point()+ggtitle("kegg+mito pathways ihd-dm vs ihd-no dm")+xlab("neg_log10_pval in ind-dm vs donor")+ylab("neg_log10_pval in ind-no dm vs donor")+theme_bw())

library(ggrepel)
gg=ggplot(plotmerge,aes(pvalue.x,pvalue.y,label=Description))+geom_point()+ggtitle("kegg+mito pathways DCM-dm vs DCM-no dm")+xlab("neg_log10_pval in DCM-dm vs donor")+ylab("neg_log10_pval in DCM-no dm vs donor")+theme_bw()+theme(aspect.ratio = 1)
#gg=ggplot(plotmerge,aes(pvalue.x,pvalue.y,label=Description,color=mito))+geom_point()+ geom_text_repel(aes(x=pvalue.x,y=pvalue.y,label=Description),subset(plotmerge,plotmerge$annotation==1), size=3)+ggtitle("kegg+mito pathways ihd-dm vs ihd-no dm")+xlab("neg_log10_pval in ind-dm vs donor")+ylab("neg_log10_pval in ind-no dm vs donor")+ scale_color_manual(values=c("black", "seagreen"))+theme_bw()+theme(aspect.ratio = 1)
ggplotly(gg)